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Results for “Dislocation Segmentation”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Ion-irradiation-induced microstructural modifications in ferritic/martensitic steel T91

In this paper, in situ transmission electron microscopy investigations were carried out to study the microstructural evolution of ferritic/martensitic steel T91 under 1 MeV Krypton ion irradiation up to 4.2 x 10(15) ions/cm(2) at 573 K, 673 K, and 773 K. At 573 K, grown-in defects are strongly modified by black dot loops, and dislocation networks together with black-dot loops were observed after irradiation. At 673 K and 773 K, grown-in defects are only partially modified by dislocation loops; isolated loops and dislocation segments were commonly found after irradiation. Post irradiation examination indicates that at 4.2 x 1015 ions/cm(2), about 51% of the loops were a(0)/2 < 111 > type for the 673 K irradiation, and the dominant loop type was a(0)< 100 > for the 773 K irradiation. Finally, a dispersed barrier hardening model was employed to estimate the change in yield strength, and the calculated ion data were found to follow the similar trend as the existing neutron data with an offset of 100-150 MPa. (C) 2017 Elsevier B.V. All rights reserved.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Geometrically necessary dislocation fingerprints of dislocation loop absorption at grain boundaries

Here we present a numerical methodology to compute the Nye-tensor fingerprints of dislocation loop absorption at grain boundaries (GBs) for comparison with TEM observations of irradiated polycrystals. Our approach links atomistic simulations of self-interstitial atom (SIA) prismatic loops gliding toward and interacting with GBs in body-centered cubic iron with experimentally extracted geometrically necessary dislocation (GND) maps to facilitate the interpretation of damage processes. The Nye-tensor analysis is strongly mesh-size dependent—corresponding to resolution-dependent TEM observations. The method computes GND fingerprints from discretized dislocation line segments extracted from molecular dynamics simulations of dislocation loops being absorbed at a GB. Specifically, we perform MD simulation of prismatic loops of two diameters and monitor the three stages of the absorption process: loop glide, the partial, and full absorption of the loops at a [1 0 0] symmetric tilt GB. These methods provide a framework for future investigations of the nature of defect absorption by grain boundaries under irradiation conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Analytical integration of the tractions induced by non-singular dislocations on an arbitrary shaped triangular quadratic element

Here, an analytical model is proposed to evaluate the nodal force induced by a segment of dislocation upon an arbitrary shaped triangular element. This calculation is required in hybrid methods that associate dislocation dynamics to boundary or finite element to solve simultaneously the evolution of large ensembles of dislocations with complex boundary conditions. Nodal forces are defined as the triple integration of the unbalanced traction field induced by a straight dislocation upon the surface of the element. Following our previous approach (Queyreau et al 2014 Modelling Simul. Mater. Sci. Eng. 22 035004) on a simpler geometry and in the case of linear isotropic elasticity, triple integrals are solved by sequences of integration by parts that exhibit recurrence relations. The traction field is defined and finite everywhere even at the core of dislocations, thanks to the use of the non-singular stress expression formulated by Cai et al (2006 J. Mech. Phys. Solids 54 561–587). The nodal force expressions can be used when considering both a single convolution or double convolution of the Green's function with the core distribution. A solution is also proposed for the case of a semi-infinite segment through the study of the asymptotic behavior of the analytical expressions. The proposed approach is exact and very computationally efficient. The choice of an arbitrary shaped triangular element and quadratic shape functions allow the consideration of complex geometries and comply with automatic meshing procedures. These analytical expressions could also be employed to estimate dislocation interactions with interfaces.

42 ENGINEERING↗
DOE OSTI2025

Dani Sleight Intern Poster

The NRDS Portal is a login-based data storage solution and science data gateway for researchers to centralize and analyze data before publication. Users can upload, edit, review, and approve their own datasets within the site to eventually be published for public use on the main NRDS site. More development was needed to extend NRDS Portal with new Artificial Intelligence features.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

A data driven approach for cross-slip modelling in continuum dislocation dynamics

Cross-slip is a thermally activated process by which screw dislocation changes its glide plane to another slip plane sharing the same Burgers vector. The rate at which this process happens is determined by a Boltzmann type expression that is a function of the screw segment length and the stress acting on the dislocation. In continuum dislocation dynamics (CDD), the information regarding the length of the screw dislocation segment and local stress state on dislocations are lost due to the coarse-grained representation of the density. Here, in this work, a data driven approach to characterize the lost information by analyzing the discrete dislocation configurations is proposed to enable cross-slip modeling in the CDD framework in terms of the coarse-grained dislocation density and stress fields. The analysis showed that the screw segment length follows an exponential distribution, and the stress fluctuations, defined as the difference between the stress on the dislocations and the mean field stress in CDD, follows a Lorentzian distribution. A novel approach for cross slip implementation in CDD employing the screw segment length and stress fluctuation statistics was proposed and rigorously tested by comparing the CDD cross-slip rates with discrete dislocation dynamics (DDD) rates. This approach has been applied in conjunction with three cross-slip models used in DDD simulations differing mainly in the functional form of cross slip activation energy. It was found that different cross-slip activation energy formulations yielded different cross-slip rates, yet the effect on mechanical stress-strain response and dislocation density evolution was minimal for the [001] type loading.

42 ENGINEERING↗
DOE OSTI2024

Post Irradiation Examination Dislocation Defect Detection Software

This software provides dislocation-type defect identification and segmentation using a standard open source computer vision model, YOLOv8, that leverages transfer learning to create a highly effective dislocation defect quantification tool while using only a minimal number of expert annotated micrographs for training. This model demonstrates the ability to segment both dislocation lines and loops concurrently in micrographs with high pixel noise levels and on multiple alloys. It includes multiple layers of frozen layers used for transfer learning from multidisciplinary data and is extensible to alloys that are not included in the training dataset.

Anderson, MatthewW↗
DOE OSTI2025

Quantifying dislocation-type defects in post irradiation examination via transfer learning

The quantitative analysis of dislocation-type defects in irradiated materials is critical to materials characterization in the nuclear energy industry. The conventional approach of an instrument scientist manually identifying any dislocation defects is both time-consuming and subjective, thereby potentially introducing inconsistencies in the quantification. This work approaches dislocation-type defect identification and segmentation using a standard open-source computer vision model, YOLO11, that leverages transfer learning to create a highly effective dislocation defect quantification tool while using only a minimal number of annotated micrographs for training. This model demonstrates the ability to segment both dislocation lines and loops concurrently in micrographs with high pixel noise levels and on two alloys not represented in the training set. Inference of dislocation defects using transmission electron microscopy on three different irradiated alloys relevant to the nuclear energy industry are examined in this work with widely varying pixel noise levels and with completely unrelated composition and dislocation formations for practical post irradiation examination analysis. Code and models are available at https://github.com/idaholab/PANDA.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Semi-Automated, Object-Based Tomography of Dislocation Structures

The characterization of the three-dimensional arrangement of dislocations is important for many analyses in materials science. Dislocation tomography in transmission electron microscopy is conventionally accomplished through intensity-based reconstruction algorithms. Although such methods work successfully, a disadvantage is that they require many images to be collected over a large tilt range. Here, we present an alternative, semi-automated object-based approach that reduces the data collection requirements by drawing on the prior knowledge that dislocations are line objects. Our approach consists of three steps: (1) initial extraction of dislocation line objects from the individual frames, (2) alignment and matching of these objects across the frames in the tilt series, and (3) tomographic reconstruction to determine the full three-dimensional configuration of the dislocations. Drawing on innovations in graph theory, we employ a node-line segment representation for the dislocation lines and a novel arc-length mapping scheme to relate the dislocations to each other across the images in the tilt series. We demonstrate the method for a dataset collected from a dislocation network imaged by diffraction-contrast scanning transmission electron microscopy. Based on these results and a detailed uncertainty analysis for the algorithm, we discuss opportunities for optimizing data collection and further automating the method.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Effects of temperature on surface-controlled dislocation multiplication in body-centered-cubic metal nanowires

Recent computational studies revealed that screw dislocations in body-centered-cubic (bcc) metal nanowires can self-multiply through cross-slip near the free surface. This unique process was termed surface-controlled dislocation multiplication (SCDM). In bcc metals, screw dislocation motion and its cross-slip behavior are often related to thermally activated processes; due to this relation, SCDM is expected to be highly temperature-sensitive. In this study, therefore, we investigated how temperature influences the SCDM in bcc molybdenum and niobium nanowires using atomistic simulations. Regardless of the difference in lattice resistance at a given temperature, both systems show similar trends of critical shear stress of SCDM with respect to temperature. Further, the temperature dependence was found to be divided into three different regimes; (1) lattice-resistance-dominant; (2) segmentation-dominant; (3) steady-state segmentation. The presence of these three regimes will be discussed in terms of the temperature-dependence of the lattice resistance and the dynamics of dislocation segmentation in the nano-scale volume. Our results provide a fundamental understanding of screw dislocation behavior in bcc metals at the nanometer scale and varying temperatures.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Van der Waals Nanowires with Continuously Variable Interlayer Twist and Twist Homojunctions

Abstract Moiré patterns at van der Waals interfaces between twisted 2D crystals give rise to distinct optoelectronic excitations, as well as, narrowly dispersive bands responsible for correlated electron phenomena. Contrasting with the conventional, mechanically stacked planar twist moirés, recent work shows twisted van der Waals interfaces spontaneously formed in nanowires of layered crystals, where Eshelby twist due to axial screw dislocations stabilizes a chiral structure with small interlayer rotation. Here, the realization of tunable twist in germanium(II) sulfide (GeS) van der Waals nanowires is reported. Tapered nanowires host continuously variable interlayer twist. Homojunctions between dislocated (chiral) and defect‐free (achiral) segments are obtained by triggering the emission of axial dislocations during growth. Measurements across such junctions, implemented here using local absorption and luminescence spectroscopy, provide a convenient tool for detecting twist effects. The results identify a versatile system for 3D twistronics, probing moiré physics, and for realizing moiré architectures without equivalent in planar systems.

Sutter, Peter↗
DOE OSTI2023

Chirality and dislocation effects in single nanostructures probed by whispering gallery modes

Nanostructures such as nanoribbons and -wires are of interest as components for building integrated photonic systems, especially if their basic functionality as dielectric waveguides can be extended by chiroptical phenomena or modifications of their optoelectronic properties by extended defects, such as dislocations. However, conventional optical measurements typically require monodisperse (and chiral) ensembles, and identifying emerging chiral optical activity or dislocation effects in single nanostructures has remained an unmet challenge. Here we show that whispering gallery modes can probe chirality and dislocation effects in single nanowires. Wires of the van der Waals semiconductor germanium(II) sulfide (GeS), obtained by vapor–liquid–solid growth, invariably form as growth spirals around a single screw dislocation that gives rise to a chiral structure and can modify the electronic properties. Cathodoluminescence spectroscopy on single tapered GeS nanowires containing joined dislocated and defect-free segments, augmented by numerical simulations and ab-initio calculations, identifies chiral whispering gallery modes as well as a pronounced modulation of the electronic structure attributed to the screw dislocation. Further, our results establish chiral light-matter interactions and dislocation-induced electronic modifications in single nanostructures, paving the way for their application in multifunctional photonic architectures.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Prismatic-to-basal plastic slip transition in zirconium

Here, we model three-dimensional dislocation ensembles in Zr pillars using newly developed mobility laws for dislocations on prismatic and basal planes. The effects of the loading orientation and temperature on the micro-scale mechanical response of single crystals are systematically explored. Easy dislocation glide is observed on prismatic planes, while hard glide occurs on basal planes. By selection of sample temperature and loading orientation, we show that the transition from prismatic to basal glide of dislocation ensembles can be controlled. We also show that the presence of dislocation cross-slip strongly promotes prismatic glide as a result of prismatic/basal cross-slip energy asymmetry. Cross-slip does not alter the occurrence of plastic slip transition but only induces a shift towards higher temperatures. The preponderance of plastic slip on basal or prismatic planes is found to be mediated by a transitional dislocation, which is composed of glissile segments on parallel prismatic planes connected by glissile super-jogs on basal planes. These findings provide a mechanistic understanding of experiments and highlight the significance of transitional prismatic-basal dislocations on the macroscopic characteristics of plasticity in HCP crystals.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Dislocation Networks and the Microstructural Origin of Strain Hardening

In this paper, when metals plastically deform, the density of line defects called dislocations increases and the microstructure is continuously refined, leading to the strain hardening behavior. Using discrete dislocation dynamics simulations, we demonstrate the fundamental role of junction formation in connecting dislocation microstructure evolution and strain hardening in face-centered cubic (fcc) Cu. The dislocation network formed consists of line segments whose lengths closely follow an exponential distribution. This exponential distribution is a consequence of junction formation, which can be modeled as a one-dimensional Poisson process. According to the exponential distribution, two non-dimensional parameters control microstructure evolution, with the hardening rate dictated by the rate of stable junction formation. Among the types of junctions in fcc crystals, we find that glissile junctions make the dominant contribution to strain hardening.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Dislocation nano-hydrides in nickel: Nucleation, evolution and effects on dislocation behaviors

Nano-hydrides have been predicted to precipitate at the core of edge dislocations in the Ni-H system, a mechanism that may promote hydrogen embrittlement. However, nano-hydride nucleation, growth, and effects on dislocation behavior have seldom been explored. This work combines molecular dynamics grand canonical Monte Carlo (MD-GCMC) simulations and continuum modeling to uncover a wide range of phenomena linked to dislocation nano-hydrides. Simulations reveal that nano-hydrides can be stabilized at dislocation cores with all character angles, including screw segments, due to the hydrostatic stresses around the cores of the Shockley partials. Nano-hydride nucleation takes place in these regions, and growth is dictated by the character angle θ of the perfect dislocation. The equilibrium stacking fault width d eq varies dynamically to increase the local hydrostatic stress field and facilitate the formation of the nano-hydride, forming a constriction-like feature and leading to three distinct behaviors: d eq decreases for θ > 30°, d eq remains unchanged for θ = 30°, and d eq increases for θ < 30°. Remote hydrostatic and Escaig stresses are also shown to influence the nucleation stage, implying stress concentrations such as those ahead of crack tips may facilitate nano-hydride precipitation. Moreover, we identify a new hydrogen-induced 60° dislocation reaction that emits a Shockley partial on a conjugate plane, with potential implications for twin nucleation. Testable predictions from this study are then used to reinterpret previous results from the literature. In conclusion, these findings provide a comprehensive framework to assess nano-hydride formation and evolution at dislocations in nickel and other face-centered cubic metals, with important implications to hydrogen embrittlement.

Dislocations↗
DOE OSTI2016

An online replanning method using warm start optimization and aperture morphing for flattening-filter-free beams

Purpose: In a situation where a couch shift for patient positioning is not preferred or prohibited (e.g., MR-linac), segment aperture morphing (SAM) can address target dislocation and deformation. For IMRT/VMAT with flattening-filter-free (FFF) beams, however, SAM method would lead to an adverse translational dose effect due to the beam unflattening. Here the authors propose a new two-step process to address both the translational effect of FFF beams and the target deformation. Methods: The replanning method consists of an offline and an online step. The offline step is to create a series of preshifted-plans (PSPs) obtained by a so-called “warm start” optimization (starting optimization from the original plan, rather than from scratch) at a series of isocenter shifts. The PSPs all have the same number of segments with very similar shapes, since the warm start optimization only adjusts the MLC positions instead of regenerating them. In the online step, a new plan is obtained by picking the closest PSP or linearly interpolating the MLC positions and the monitor units of the closest PSPs for the shift determined from the image of the day. This two-step process is completely automated and almost instantaneous (no optimization or dose calculation needed). The previously developed SAM algorithm is then applied for daily deformation. The authors tested the method on sample prostate and pancreas cases. Results: The two-step interpolation method can account for the adverse dose effects from FFF beams, while SAM corrects for the target deformation. Plan interpolation method is effective in diminishing the unflat beam effect and may allow reducing the required number of PSPs. The whole process takes the same time as the previously reported SAM process (5–10 min). Conclusions: The new two-step method plus SAM can address both the translation effects of FFF beams and target deformation, and can be executed in full automation except the delineation of target contour required by the SAM process.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

ADVANCED-STEM-BASED DEEP LEARNING FOR SEMANTIC SEGMENTATION OF DEFECTS IN STEELS

In this work, we developed a novel deep convolutional neural network (DCNN) model, called DefectNet, for robust and automated semantic segmentation of three crystallographic defects including line dislocations, precipitates and voids commonly observed in structural metals and alloys. In previous work, we established an experimental protocol for a diffraction contrast imaging scanning transmission electron microscopy (DCI STEM) technique and tailored it specifically for imaging defects in popular iron-based structural alloy. Thus, the DefectNet was trained over a small but high-quality DCI STEM defect image sets of a HT-9 martensitic steel before and after neutron irradiation for 111.8 dpa at 412°C. For the defect quantification that typically takes at least half an hour even for an expert, with a good model the machine learning algorithm can produce more reproducible and reliable quantification in a few seconds. This work demonstrates the feasibility of using deep learning algorithms for fast and accurate defect quantitative analysis.

Machine Learning, Radiation effects, Image analysi↗
DOE OSTI2019

DefectSegNet

DefectSegNet for semantic segmentation of defects in Steels The first and most important step of automating defect analysis is perceptual defect identification. In terms of digital image processing, semantic segmentation best emulates human recognition of defect features – it identifies the crystallographic defects and where they are located in a TEM micrograph. We solved this problem by developing DefectSegNet, a novel hybrid CNN algorithm for robust and automated semantic segmentation of three crystallographic defects, including line dislocations, precipitates and voids, that are commonly observed in structural metals and alloys.

Sainju, Rajat↗
DOE OSTI2019

Deep Learning for Semantic Segmentation of Defects in Advanced STEM Images of Steels

Crystalline materials exhibit long-range ordered lattice unit, within which resides nonperiodic structural features called defects. These crystallographic defects play a vital role in determining the physical and mechanical properties of a wide range of material systems. While computer vision has demonstrated success in recognizing feature patterns in images with well defined contrast, automated identification of nanometer scale crystallographic defects in electron micrographs governed by complex contrast mechanism is still a challenging task. Here, building upon an advanced defect imaging mode that offers high feature clarity, we introduce DefectSegNet - a new convolutional neural network (CNN) architecture that performs semantic segmentation of three common crystallographic defects in structural alloys: dislocation lines, precipitates and voids. Results from supervised training of a small set of high-quality defect images of steels show high pixel-wise accuracy across all three types of defects: 91.60 ± 1.77% on dislocations, 93.39 ± 1.00% on precipitates, and 98.85 ± 0.56% on voids. We discuss the sources of uncertainties in CNN prediction, and the training data in terms of feature density, representation and homogeneity and their effects on deep learning performance. Further defect quantification using DefectSegNet prediction outperforms human expert average, presenting a promising new workflow for fast and statistically meaningful quantification of materials defects.

36 MATERIALS SCIENCE↗